Tung Wah College

Wah, Hong Kong

Tung Wah College

Wah, Hong Kong
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Zhan G.,Tung Wah College | Wong A.H.K.,University of Hong Kong
ACM International Conference Proceeding Series | Year: 2017

In this paper, we developed conceptual frameworks for studying consumer adoption of Wi-Fi network and its security measures. We collected data from users of Wi-Fi network in a large insurance firm and universities in Hong Kong. The findings indicate that security knowledge is a significant mediator of attitude effect on Wi-Fi adoption. Furthermore, consumer adoption of security measures is influenced by security knowledge and perceived threat.

Ching S.S.Y.,Hong Kong Polytechnic University | Martinson I.M.,University of California at San Francisco | Wong T.K.S.,Tung Wah College
Qualitative Health Research | Year: 2012

Based on a study exploring the phenomenon of coping among Hong Kong Chinese women afflicted with breast cancer, from diagnosis to completion of treatment, we report the findings on meaning making by the informants. Using the grounded theory method, we conducted 35 interviews with 24 women suffering from breast cancer. Among them, we followed and interviewed 5 women thrice, from diagnosis to 3 months after completion of treatment. We noted the evolution of reframing as the key category in the adjustment process through which the women identified meaning at different points of time in the cancer experience, to achieve different outcomes. Chinese women identified a sustaining force from minimizing social disturbance during treatment. The integration of cancer into their lives after completion of treatment was achieved through positive transformation in their philosophy of life and social relationships. Nurses should aim to understand the cancer patients' interpretation of the situation, explore personally meaningful sustaining forces, and reflect on their cancer experience. © SAGE Publications 2012.

Zhou L.,Macau University of Science and Technology | Lai K.K.,City University of Hong Kong | Lai K.K.,North China Electrical Power University | Yen J.,Tung Wah College
Computers and Mathematics with Applications | Year: 2012

Accurate prediction of corporate financial distress is very important for managers, creditors and investors to take correct measures to reduce loss. Many quantitative methods have been employed to develop empirical models for predicting corporate bankruptcy. However, there is so much information disclosed in the companies' financial statements, what information should be selected for building the empirical models with objective to maximize the predictive accuracy. In this study, more than 20 models based on six features ranking strategies are tested on North American companies and Chinese listed companies. The experimental results are helpful to develop financial models by choosing the proper quantitative methods and features selection strategy. © 2012 Elsevier Ltd. All rights reserved.

Zhou L.,Macau University of Science and Technology | Lai K.K.,City University of Hong Kong | Yen J.,Tung Wah College
International Journal of Systems Science | Year: 2014

Due to the economic significance of bankruptcy prediction of companies for financial institutions, investors and governments, many quantitative methods have been used to develop effective prediction models. Support vector machine (SVM), a powerful classification method, has been used for this task; however, the performance of SVM is sensitive to model form, parameter setting and features selection. In this study, a new approach based on direct search and features ranking technology is proposed to optimise features selection and parameter setting for 1-norm and least-squares SVM models for bankruptcy prediction. This approach is also compared to the SVM models with parameter optimisation and features selection by the popular genetic algorithm technique. The experimental results on a data set with 2010 instances show that the proposed models are good alternatives for bankruptcy prediction. © 2014 Taylor and Francis.

Chung J.W.Y.,The Hong Kong Institute of Education | Zeng Y.,Guangzhou University | Wong T.K.S.,Tung Wah College
Pain Physician | Year: 2013

Background: Low back pain (LBP) is one of the most common health problems in adults. The impact of LBP on the individual can cause loss of health status in the form of symptoms and loss of function related to pain in the back; limitation of daily, leisure, and/or strenuous activities, and disability. LBP also poses an economic burden to society, mainly in terms of one of the most common reasons for seeking medical care (direct treatment costs), and accounts for the large number of work days lost (indirect costs). To reduce the impact of LBP on adults, drug therapy is the most frequently recommended intervention. Over the last decade, a substantial number of randomized clinical trials of drug therapy for LBP have been published. Objective: To determine the effectiveness of drug therapy for the treatment of chronic nonspecific low back pain (CNLBP). Study Design: Systematic review and meta-analysis Methods: A systematic review and meta-analysis of randomized controlled trials was conducted. Five databases (Medline, CINAHL, Science Direct, CAJ Full-text Database, and Cochrane databases) were searched for articles published from 2002 to 2012. The eligibility criteria were randomized trials and double-blind controlled trials of oral or injection drug therapy for CNLBP in subjects who were aged at least 18 years old, published in English or Chinese. Two independent reviewers extracted the data. Results: A total of 25 drug therapy trials were included. cyclo-oxygenase-2 (COX-2) nonsteroidal anti-inflammatory drugs (NSAIDs), tramadol, and opioids were commonly used. Only 5 trials studied the efficacy of adjuvant analgesics of antiepileptics (n = 1) and antidepressants (n = 4) for CNLBP. The standardized mean difference (SMD) for COX-2 NSAIDs in pain relief was -12.03 (95% confidence interval [CI]: -15.00 to -9.06). The SMD for tramadol in pain relief was -1.72 (95% CI: -3.45 to 0.01). As the 95% CI crossed 0, this effect size was not considered statistically significant. The SMD for the overall effects of opioids in pain relief was -5.18 (95% CI: -8.30 to -2.05). The SMD for the partial opioid agonist drug in pain relief was -7.46 (95% CI: -11.87 to -3.04). Limitations: The follow-up periods of these included trials in the meta-analysis ranged from 4 to 24 weeks. The difference of follow-up periods influenced how study outcomes were recorded. These included trials also had significant differences in patient selections. Some trials may actually include CNLBP patients with neuropathic pain, as not having focal neurological findings or signs does not mean that the pain is not neuropathic. Consequently, different pain conditions may influence patients who responded to the same drug and then influence pooled estimates of treatment effect size. Conclusion: This review endorses the use of COX-2 NSAIDs as the first-line drugs for CNLBP. Tramadol shows no statistically significant effect on pain relief, but has small effect sizes in improving functioning. Among included opioid therapy studies, the overall effects of opioids and the partial opioids agonist drug had statistically significant treatment effects in pain relief for CNLBP patients.

Tang A.C.Y.,Caritas Medical Center | Chung J.W.Y.,The Hong Kong Institute of Education | Chung J.W.Y.,Tung Wah College | Wong T.K.S.,Hong Kong Polytechnic University
Evidence-based Complementary and Alternative Medicine | Year: 2012

In view of lacking a quantifiable traditional Chinese medicine (TCM) pulse diagnostic model, a novel TCM pulse diagnostic model was introduced to quantify the pulse diagnosis. Content validation was performed with a panel of TCM doctors. Criterion validation was tested with essential hypertension. The gold standard was brachial blood pressure measured by a sphygmomanometer. Two hundred and sixty subjects were recruited (139 in the normotensive group and 121 in the hypertensive group). A TCM doctor palpated pulses at left and right cun, guan, and chi points, and quantified pulse qualities according to eight elements (depth, rate, regularity, width, length, smoothness, stiffness, and strength) on a visual analog scale. An artificial neural network was used to develop a pulse diagnostic model differentiating essential hypertension from normotension. Accuracy, specificity, and sensitivity were compared among various diagnostic models. About 80% accuracy was attained among all models. Their specificity and sensitivity varied, ranging from 705 to nearly 90%. It suggested that the novel TCM pulse diagnostic model was valid in terms of its content and diagnostic ability. Copyright © 2012 Anson Chui Yan Tang et al.

Zhu L.-X.,Hong Kong Polytechnic University | Ho S.-C.,Hong Kong Polytechnic University | Wong T.K.,Tung Wah College
Journal of Evidence-Based Medicine | Year: 2013

Background and Objective: Regular exercise has been shown to be beneficial to patients with heart disease. Previous studies have indicated that health education can effectively increase participants' physical activity. However, no systematic review was conducted to evaluate the effectiveness of health education programs on changing exercise behavior among patients with heart disease. The aim of this study was to examine the effectiveness of health education programs on exercise behavior among heart disease patients. Method: Potential studies were retrieved in the Cochrane Central Register of Controlled Trials, MEDLINE, CINAHL, EMbase, PsycINFO, the British Nursing Index and Archive, Science Direct, and ERIC via EBSCOhost. Meta-analysis was done using the random-effect model. Results: Thirty-seven studies were identified. Only 12 studies delivered health education based on various theories/models. Twenty-eight studies were included in the meta-analyses. The results showed that health education had significantly positive effects on exercise adherence (risk ratio = 1.35 to 1.48), exercise duration (SMD = 0.25 to 0.69), exercise frequency (MD = 0.54 to 1.46 session/week), and exercise level (SMD = 0.25), while no significant effects were found on exercise energy expenditure and cognitive exercise behavior. Conclusion: Health education has overall positive effects on changing exercise behavior among heart disease patients. Few theoretical underpinning studies were conducted for changing exercise behavior among heart disease patients. The findings suggest that health education improves exercise behavior for heart disease patients. Health professionals should reinforce health education programs for them. © 2013 Chinese Cochrane Center, West China Hospital of Sichuan University and Wiley Publishing Asia Pty Ltd.

He K.,City University of Hong Kong | Lai K.K.,North China Electrical Power University | Yen J.,Tung Wah College
Procedia Computer Science | Year: 2010

Despite the active exploration of linear and nonlinear modeling of exchange rates, there is no consensus on the optimal forecasting model other than the traditional random walk and ARMA benchmark models in the literature. Given the increasing recognition of heterogeneous market structure, this paper proposes an alternative Slantlet denoising based hybrid methodology that attempts to incorporate the linear and nonlinear data features. The recently emerging Slantlet analysis is introduced to separate the linear data features as it constructs filters with varying lengths at different scales and has more appealing time localization features than the normal wavelet analysis. Meanwhile, the Least Squares Support Vector Regression (LSSVR) is used to model and correct for the empirical errors nonlinear in nature. As empirical studies were conducted in the Euro exchange rate market, the performance of the proposed algorithm was compared with those of benchmark models including random walk, ARMA and LSSVR models. The proposed algorithm outperforms the benchmark models. More importantly the proposed methodology explores and offers deeper insights as to the underlying data generating process. © 2010 Published by Elsevier Ltd.

He K.,Hunan University of Science and Technology | Lai K.K.,City University of Hong Kong | Yen J.,Tung Wah College
Energy Economics | Year: 2011

With the increasing level of volatility in the crude oil market, the transient data feature becomes more prevalent in the market and is no longer ignorable during the risk measurement process. Since there are multiple representations for these transient data features using a set of bases available, the sparsity measure based Morphological Component Analysis (MCA) model is proposed in this paper to find the optimal combinations of representations to model these transient data features. Therefore, this paper proposes a MCA based hybrid methodology for analyzing and forecasting the risk evolution in the crude oil market. The underlying transient data components with distinct behaviors are extracted and analyzed using MCA model. The proposed algorithm incorporates these transient data features to adjust for conservative risk estimates from traditional approach based on normal market condition during its risk measurement process. The reliability and stability of Value at Risk (VaR) estimated improve as a result of finer modeling procedure in the multi frequency and time domain while maintaining competent accuracy level, as supported by empirical studies in the representative West Taxes Intermediate (WTI) and Brent crude oil market. © 2011 Elsevier B.V.

Dong G.,City University of Hong Kong | Lai K.K.,North University of China | Yen J.,Tung Wah College
Procedia Computer Science | Year: 2010

Many credit scoring techniques have been used to build credit scorecards. Among them, logistic regression model is the most commonly used in the banking industry due to its desirable features (e.g., robustness and transparency). Although some new techniques (e.g., support vector machine) have been applied to credit scoring and shown superior prediction accuracy, they have problems with the results interpretability. Therefore, these advanced techniques have not been widely applied in practice. To improve the prediction accuracy of logistic regression, logistic regression with random coefficients is proposed. The proposed model can improve prediction accuracy of logistic regression without sacrificing desirable features. It is expected that the proposed credit scorecard building method can contribute to effective management of credit risk in practice.

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